Exploring the Panorama of Anxiety Levels: A Multi-Scenario Study Based on Human-Centric Anxiety Level Detection and Personalized Guidance
Project Overview
The document explores the application of generative AI, particularly the GPT-4 model, in education to address anxiety management through simulated conversations. It details a study that developed a comprehensive knowledge base on anxiety, showcasing how different transformer models can effectively predict anxiety levels. The findings emphasize the potential of personalized guidance and mental health support within educational settings, highlighting the role of AI in facilitating tailored interventions for students experiencing anxiety. By simulating various conversational scenarios, the study demonstrates how generative AI can provide immediate support and resources, ultimately aiming to enhance student well-being and create a more supportive learning environment. The outcomes suggest that integrating AI tools into educational frameworks can significantly improve mental health resources and foster resilience among students.
Key Applications
GPT-4-generated multi-scenario simulated conversations for anxiety detection
Context: Educational settings involving students and teachers, where anxiety levels are assessed through conversations.
Implementation: Simulated conversations generated by GPT-4 covering different anxiety levels in educational contexts.
Outcomes: Achieved over 94% accuracy in predicting anxiety levels, providing personalized advice tailored to individual situations.
Challenges: Limitations in dataset diversity and potential scalability of the solution.
Implementation Barriers
Technical Barriers
The complexity of life circumstances affecting mental health, which complicates the effectiveness of digital health interventions.
Proposed Solutions: Further research is needed to tailor interventions for diverse populations with varying psychosocial complexities.
Project Team
Longdi Xian
Researcher
Junhao Xu
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Longdi Xian, Junhao Xu
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai